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A Hierarchical Skull Point Cloud Registration Method
- Source :
- IEEE Access, Vol 7, Pp 132609-132618 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.
- Subjects :
- feature matching
General Computer Science
Computer science
Physics::Medical Physics
0211 other engineering and technologies
Point cloud
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020101 civil engineering
02 engineering and technology
spin image
0201 civil engineering
k-nearest neighbors algorithm
Spatial reference system
021105 building & construction
medicine
General Materials Science
Computer vision
geometric feature constraints
business.industry
random sampling
General Engineering
ICP algorithm
Skull
medicine.anatomical_structure
Skull registration
Computer Science::Computer Vision and Pattern Recognition
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....3b9cf6f38f30ad6474b8ad8de20d4b13